2 Types of Corrupted Data 2 2.1 Data with Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.1 Bernoulli-Type Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.2 Markov-Type Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.3 Data Distributed by Heavy-Tailed Distributions . . . . . . . . . . . . . . 3 2.1.4 ε-Contamin...

BACKGROUND Participants' non adherence to protocol affects data quality. In longitudinal studies, this leads to outliers that can be present at the level of the population or the individual. The purpose of the present study is to elaborate a method for detection of outliers in a study of cognitive ageing. METHODS In the Whitehall II study, data on a cognitive test battery have been collected ...

Dataset with Outliers causes poor accuracy in future analysis of data mining tasks. To improve the performance of mining task, it is necessary to detect and revamp of outliers which are there in the dataset. Existing techniques like ARMA (Auto-Regressive Moving Average), ARIMA (AutoRegressive Integrated Moving Average) and Multivariate Linear Gaussian state space model don't consider the p...

Detecting outliers from high-dimensional data is a challenge task since outliers mainly reside in various lowdimensional subspaces of the data. To tackle this challenge, subspace analysis based outlier detection approach has been proposed recently. Detecting outlying subspaces in which a given data point is an outlier facilitates a better characterization process for detecting outliers for high...

Sensors are increasingly part of our daily lives: motion detection, lighting control, and energy consumption all rely on sensors. Combining this information into, for instance, simple and comprehensive graphs can be quite challenging. Dimensionality reduction is often used to address this problem, by decreasing the number of variables in the data and looking for shorter representations. However...

AIMS To create clinically relevant normative flow cytometry data for understudied benign lymph nodes and characterise outliers. METHODS Clinical, histological and flow cytometry data were collected and distributions summarised for 380 benign lymph node excisional biopsies. Outliers for kappa:lambda light chain ratio, CD10:CD19 coexpression, CD5:CD19 coexpression, CD4:CD8 ratios and CD7 loss w...

Noise clustering, as a robust clustering method, performs partitioning of data sets reducing errors caused by outliers. Noise clustering defines outliers in terms of a certain distance, which is called noise distance. The probability or membership degree of data points belonging to the noise cluster increases with their distance to regular clusters. The main purpose of noise clustering is to re...

Multi-dimensional scaling (MDS) plays a central role in data-exploration, dimensionality reduction and visualization. State-of-the-art MDS algorithms are not robust to outliers, yielding significant errors in the embedding even when only a handful of outliers are present. In this paper, we introduce a technique to detect and filter outliers based on geometric reasoning. We test the validity of ...

Recently, Vogelsang (1999) proposed a method to detect outliers which explicitly imposes the null hypothesis of a unit root. It works in an iterative fashion to select multiple outliers in a given series. We show, via simulations, that under the null hypothesis of no outliers, it has the right size in ...nite samples to detect a single outlier but when applied in an iterative fashion to select ...

Outliers can be defined simply as an observation (or a subset of observations) that is isolated from the other observations in the data set. There are two main reasons that motivate people to find outliers; the first is the researchers intention. The second is the effects of an outlier on analyses. This article does not differentiate between the various justifications for outlier detection. The...